What No Show Predictor is designed to support | Phased adoption approach | Data quality is critical to accuracy | How risk scoring works | Primary care vs. specialty care considerations | Using No Show Predictor alongside existing reminders | Scheduling strategy is always a practice decision | Your feedback helps shape No Show Predictor | Measuring success | When to reevaluate your approach
No Show Predictor helps your practice identify upcoming appointments that may be at higher risk of being missed, so your team can take proactive steps to protect revenue, reduce schedule disruption, and improve patient access to care.
This article explains how to adopt and operationalize No Show Predictor over time. For technical setup and feature navigation, refer to No Show Predictor.
What No Show Predictor is designed to support
The No Show Predictor assigns a low, medium, or high risk score to upcoming appointments, helping your team:
- Focus outreach on patients who may need additional attention
- Reduce last-minute schedule gaps
- Improve provider utilization
- Make more informed scheduling decisions
- Improve patient access by preventing avoidable missed visits
No Show Predictor works best when paired with consistent front-office workflows and accurate appointment documentation.
Phased adoption approach
We recommend adopting No Show Predictor in phases, so your team can build confidence in the predictions before making any operational changes.
Phase 1: Turn on and observe (weeks 1–2)
Goal: Become familiar with No Show Predictor and validate the scoring.
Best practices
- View risk scores on your schedule without changing current workflows
- Compare flagged appointments with staff intuition and historical patterns
- Discuss internally whether the risk levels align with what your team already sees
At this stage
- No workflow changes are required
- No scheduling adjustments are expected
- This phase is for observation and learning only
Phase 2: Validate and prioritize outreach (weeks 3–4)
Goal: Begin using risk insights to focus outbound communication.
Best practices
- Track which flagged appointments result in completed visits versus no-shows
- Use risk levels to prioritize confirmation calls or messages
- Continue logging outreach consistently for team visibility
- Gather feedback from schedulers and front-office staff on how the predictions perform
- Success in this phase may look like
- Fewer surprise no-shows
- More confidence in the risk levels
- More efficient use of outreach time
Phase 3: Optimize Scheduling Decisions (Week 5+)
Goal: Use risk insights to support a higher-level scheduling strategy.
At this stage, some practices choose to use the No Show Predictor to inform broader scheduling decisions, which may include:
- Adjusting how aggressively high-risk appointments are confirmed
- Evaluating whether certain visit types or providers are more schedule-sensitive
- Considering whether selective double booking makes sense for their practice
Any decisions regarding schedule density or double-booking remain entirely at your practice's discretion, and should be guided by provider preferences, operational capacity, patient experience standards, and overall risk tolerance. No Show Predictor provides data to inform decisions; it does not dictate scheduling policy.
Data quality is critical to accuracy
No Show Predictor depends on clean, consistent scheduling data. Accuracy improves when appointment outcomes are documented correctly and consistently.
To support reliable predictions, your team should:
- Mark true missed visits as No Show
- Avoid rescheduling when the patient did not arrive
- Clearly distinguish between
- Same-day cancellations
- True no-shows
- Advance reschedules
- Log confirmation activity when applicable
Why this matters
No Show Predictor learns from historical scheduling behavior. When no-shows are not consistently documented, the system cannot accurately identify future patterns.
How risk scoring works
To determine no-show likelihood, No Show Predictor evaluates:
- Historical attendance patterns, including individual no-show rate per patient
- Behavioral trends over time, including reschedules, cancellations, and confirmations
- Real-time outreach and communication status
- Schedule-level patterns such as clinic-wide attendance trends
- Appointment-level context, including same-day changes, confirmations, and booking lead time
No Show Predictor then assigns a low, medium, or high risk score that updates dynamically as new information becomes available.
Primary care vs. specialty care considerations
Different care models naturally tolerate different levels of scheduling risk.
- Primary care: Typically higher visit volume, greater variability, and more flexibility with schedule density.
- Specialty care: Typically lower visit volume per provider, longer visit lengths, longer waitlists, and lower tolerance for missed appointments.
Each practice should determine internally:
- Which appointment types are most sensitive to missed visits
- Which providers prefer conservative versus flexible scheduling
- Where proactive outreach has the greatest operational impact
Using No Show Predictor alongside existing reminders
No Show Predictor doesn't replace your existing reminder workflows. It helps your team prioritize where additional attention may be most helpful.
Recommended approach
- Maintain your standard reminder cadence
- Layer additional confirmations for higher-risk appointments
- Focus staff time where it can prevent the most disruption
This approach allows your team to work more efficiently without adding unnecessary outreach volume.
Scheduling strategy is always a practice decision
Some practices use No Show Predictor insights solely to prioritize outreach, while others may also evaluate whether selective double-booking is appropriate in limited circumstances.
Before deciding whether or how to apply double-booking, your practice should consider:
- Provider comfort level
- Patient experience expectations
- Appointment length and visit complexity
- Staff coverage and operational capacity
- Historical no-show rates by visit type
There is no individual recommended approach. No Show Predictor provides visibility into risk. Each practice determines how that information is applied operationally.
Your feedback helps shape No Show Predictor
As you use No Show Predictor, your feedback plays a critical role in improving the predictive model over time.
We encourage you to share:
- Instances where predictions did not align with real outcomes
- Workflow challenges that affect data quality
- Specialty-specific scheduling patterns
- Operational constraints unique to your practice
Submit feedback through your Customer Success Manager or our Support team. Your real-world input helps ensure No Show Predictor continues to evolve in a way that delivers accurate, meaningful results.
Measuring success
Practices that adopt No Show Predictor successfully often see:
- Reduced missed appointment rates
- More predictable daily schedules
- Higher provider utilization
- Improved patient access due to fewer unfilled slots
- More focused, efficient front-office outreach
When to reevaluate your approach
You may want to revisit your adoption strategy if you notice:
- Risk scores not aligning with outcomes after several weeks of observation
- Staff rescheduling instead of marking no-shows
- Confusion between cancellations and true no-shows
- Provider discomfort with any scheduling flexibility
- Inconsistent communication workflows
Our Customer Success team can help optimize your workflows and make sure your feedback is formally documented and shared with our Product team to support ongoing improvements. You can reach them by contacting Support.